Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience

Computational models in neuroscience typically contain many parameters that are poorly constrained by experimental data. Uncertainty quantification and sensitivity analysis provide rigorous procedures to quantify how the model output depends on this parameter uncertainty. Unfortunately, the application of such methods is not yet standard within the field of neuroscience. Here we present Uncertainpy, an open-source Python toolbox, tailored to perform uncertainty quantification and sensitivity analysis of neuroscience models. Uncertainpy aims to make it quick and easy to get started with uncertainty analysis, without any need for detailed prior knowledge. The toolbox allows uncertainty quantification and sensitivity analysis to be performed on already existing models without needing to modify the model equations or model implementation. Uncertainpy bases its analysis on polynomial chaos expansions, which are more efficient than the more standard Monte-Carlo based approaches. Uncertainpy is tailored for neuroscience applications by its built-in capability for calculating characteristic features in the model output. The toolbox does not merely perform a point-to-point comparison of the “raw” model output (e.g., membrane voltage traces), but can also calculate the uncertainty and sensitivity of salient model response features such as spike timing, action potential width, average interspike interval, and other features relevant for various neural and neural network models. Uncertainpy comes with several common models and features built in, and including custom models and new features is easy. The aim of the current paper is to present Uncertainpy to the neuroscience community in a user-oriented manner. To demonstrate its broad applicability, we perform an uncertainty quantification and sensitivity analysis of three case studies relevant for neuroscience: the original Hodgkin-Huxley point-neuron model for action potential generation, a multi-compartmental model of a thalamic interneuron implemented in the NEURON simulator, and a sparsely connected recurrent network model implemented in the NEST simulator

Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence

During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic regions offer potential for the development of universally protective T cell vaccine candidates. Here, we validated and characterized T cell responses to a set of minimal epitopes from these AI-identified universal hotspots. Utilizing a flow cytometry-based T cell activation-induced marker (AIM) assay, we identified 59 validated screening hits, of which 56% (33 peptides) have not been previously reported. Notably, we found that most of these novel epitopes were derived from the non-spike regions of SARS-CoV-2 (Orf1ab, Orf3a, and E). In addition, ex vivo stimulation with NIP-predicted peptides from the spike protein elicited CD8+ T cell response in PBMC isolated from most vaccinated donors. Our data confirm the predictive accuracy of AI platforms modelling bona fide immunogenicity and provide a novel framework for the evaluation of vaccine-induced T cell responses

People who use drugs show no increase in pre-existing T-cell cross-reactivity toward SARS-CoV-2 but develop a normal polyfunctional T-cell response after standard mRNA vaccination

People who use drugs (PWUD) are at a high risk of contracting and developing severe coronavirus disease 2019 (COVID-19) and other infectious diseases due to their lifestyle, comorbidities, and the detrimental effects of opioids on cellular immunity. However, there is limited research on vaccine responses in PWUD, particularly regarding the role that T cells play in the immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we show that before vaccination, PWUD did not exhibit an increased frequency of preexisting cross-reactive T cells to SARS-CoV-2 and that, despite the inhibitory effects that opioids have on T-cell immunity, standard vaccination can elicit robust polyfunctional CD4+ and CD8+ T-cell responses that were similar to those found in controls. Our findings indicate that vaccination stimulates an effective immune response in PWUD and highlight targeted vaccination as an essential public health instrument for the control of COVID-19 and other infectious diseases in this group of high-risk patients

Uncertainty quantification in neuroscience

The complexity of the nervous system has made computational science an invaluable tool in order to understand how the nervous system functions. The overarching goal of this thesis has been to develop software tools to improve areas of neuroscience that are currently lacking, which include uncertainty analysis of computational models (Paper I), data storage (Paper IV), and education (Paper V). The major area of focus has been that of uncertainty analysis. Computational models always contain parameters that describe the system to be modeled. These parameters are for various reasons often uncertain. An uncertainty analysis provides rigorous procedures to quantify how the model depends on this parameter uncertainty. To reduce the barrier of performing uncertainty analysis in neuroscience we have created a toolbox for uncertainty analysis (Paper I). We then used this toolbox on a selected set of models (Paper I, II and III). In Paper I we introduced Uncertainpy, a Python toolbox for performing uncertainty quantification and sensitivity analysis. Uncertainpy is tailored for neuroscience applications by its built-in capability for calculating characteristic features in the model output. We provided a detailed user guide for Uncertainpy and illustrated its use by showing four different case studies. In Paper II we presented a reimplementation of a model for endocrine pituitary cells in rats. We qualitatively replicated the computational results in the original publication and confirmed the key conclusions, namely that big conductance K+ (BK) ion channels are important for the bursting activity of endocrine pituitary cells in rats. Additionally, we performed an uncertainty analysis of the model using Uncertainpy, which further strengthened the findings in the original publication. In Paper III we created a computational model for endocrine pituitary cells in medaka, a species of Japanese rice fish. The reimplementation and results in Paper II were used as a basis for the computational work in this paper. We discovered that the BK conductance has the opposite effect on the action potential shape in medaka pituitary cells compared to in the rat pituitary cells in Paper II. The BK channels makes the action potentials generated in the medaka model narrower, but they make the action potentials generated in the rat model broader. An uncertainty analysis of the two models was performed in order to examine differences in the sensitivity of the models to changes in their ion channel conductances. In Paper IV we developed a specification for organizing data in a hierarchy by using file-system directories to represent the hierarchy. We used the same data abstraction as in the HDF5 file format. We provided a reference implementation in Python and described how to use this implementation. In Paper V we introduced Neuronify, an educational app for easily creating neural networks by dragging and dropping neurons onto the canvas and then simulating the networks. Neuronify is available for iOS and Android, as well as Mac, Linux, and Windows

Halo finding in Modified Gravity N-body simulations

Modified gravity theories are a popular research field in the hope that they could explain some of the unanswered questions in cosmology, for example how the universe starts, evolves and ends. One way to test proposed modified gravity models is by analyzing how the model affects the evolution of large scale structures in the universe. In the non-linear regime the only way of doing this is by performing N-body simulations, which simulates how a dynamical system of particles behave and evolve under the influence of physical forces. After performing such a simulation we wish to compare the data from the simulation with the observed universe. This cannot be done directly from the output from N-body simulations. To extract the necessary statistics a halo finding process should be performed, which determine how galaxies are grouped into halos and calculates the properties of these halos. As of yet, no one has taken into account the differences between standard general relativity and modified gravity in their halo finders, so the validity of other halo finders in the regime of modified gravity is therefore unknown. This is what has been the focus of this thesis. Here, we introduce MORPH, the first halo finder that is completely independent of the gravity model used in the N- body simulations. MORPH can analyze any dataset from a modified gravity N-body simulation. This is performed without the need for code modifications to accommodate for the modified gravity theory. As a part of this work we have examined various unbinding algorithms and their dependence on the gravity model. The main question this thesis set out to answer was whether there is a justified need for a modified gravity adjusted halo finder. The conclusion is that modified gravity must be taken into consideration when we intend to analyze halos in modified gravity datasets. However, only if the halo finders have a high unbinding percentage, making the errors from the unbinding routine larger than the current 10% error bars for halo finding

Uncertainpy: A Python toolbox for uncertainty quantification and sensitivity analysis in computational neuroscience

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The T Cell Epitope Landscape of SARS-CoV-2 Variants of Concern

During the COVID-19 pandemic, several SARS-CoV-2 variants of concern (VOC) emerged, bringing with them varying degrees of health and socioeconomic burdens. In particular, the Omicron VOC displayed distinct features of increased transmissibility accompanied by antigenic drift in the spike protein that partially circumvented the ability of pre-existing antibody responses in the global population to neutralize the virus. However, T cell immunity has remained robust throughout all the different VOC transmission waves and has emerged as a critically important correlate of protection against SARS-CoV-2 and its VOCs, in both vaccinated and infected individuals. Therefore, as SARS-CoV-2 VOCs continue to evolve, it is crucial that we characterize the correlates of protection and the potential for immune escape for both B cell and T cell human immunity in the population. Generating the insights necessary to understand T cell immunity, experimentally, for the global human population is at present a critical but a time consuming, expensive, and laborious process. Further, it is not feasible to generate global or universal insights into T cell immunity in an actionable time frame for potential future emerging VOCs. However, using computational means we can expedite and provide early insights into the correlates of T cell protection. In this study, we generated and revealed insights on the T cell epitope landscape for the five main SARS-CoV-2 VOCs observed to date. We demonstrated using a unique AI prediction platform, a significant conservation of presentable T cell epitopes across all mutated peptides for each VOC. This was modeled using the most frequent HLA alleles in the human population and covers the most common HLA haplotypes in the human population. The AI resource generated through this computational study and associated insights may guide the development of T cell vaccines and diagnostics that are even more robust against current and future VOCs, and their emerging subvariants

Differential Expression and Cell-Type Specificity of Perineuronal Nets in Hippocampus, Medial Entorhinal Cortex and Visual Cortex Examined in the Rat and Mouse

Perineuronal nets (PNNs) are specialized extracellular matrix (ECM) structures that condense around the soma and proximal dendrites of subpopulations of neurons. Emerging evidence suggests that they are involved in regulating brain plasticity. However, the expression of PNNs varies between and within brain areas. A lack of quantitative studies describing the distribution and cell-specificity of PNNs makes it difficult to reveal the functional roles of PNNs. In the current study, we examine the distribution of PNNs and the identity of PNN-enwrapped neurons in three brain areas with different cognitive functions: the dorsal hippocampus, medial entorhinal cortex (mEC) and primary visual cortex (V1). We compared rats and mice as knowledge from these species are often intermingled. The most abundant expression of PNNs was found in the mEC and V1, while dorsal hippocampus showed strikingly low levels of PNNs, apart from dense expression in the CA2 region. In hippocampus we also found apparent species differences in expression of PNNs. While we confirm that the PNNs enwrap parvalbumin-expressing (PV+) neurons in V1, we found that they mainly colocalize with excitatory CamKII-expressing neurons in CA2. In mEC, we demonstrate that in addition to PV+ cells, the PNNs colocalize with reelin-expressing stellate cells. We also show that the maturation of PNNs in mEC coincides with the formation of grid cell pattern, while PV+ cells, unlike in other cortical areas, are present from early postnatal development. Finally, we demonstrate considerable effects on the number of PSD-95-gephyrin puncta after enzymatic removal of PNNs